TY - JOUR
T1 - A Multifactorial Model of T Cell Expansion and Durable Clinical Benefit in Response to a PD-L1 Inhibitor
JF - bioRxiv
M3 - 10.1101/231316
AU - Leiserson, Mark DM
AU - Syrgkanis, Vasilis
AU - Gilson, Amy
AU - Dudik, Miroslav
AU - Funt, Samuel
AU - Snyder, Alexandra
AU - Mackey, Lester
Y1 - 2017/01/01
UR - http://biorxiv.org/content/early/2017/12/08/231316.abstract
N2 - Background: Immunotherapies such as checkpoint inhibitors have become a major success in treating patients with late-stage cancers, yet the minority of patients benefit. PD-L1 staining and mutation burden are the leading biomarkers associated with response, but each is an imperfect predictor. We seek to address this challenge by developing a multifactorial model for response to anti-PD-L1 therapy. Methods: We train a model to predict fine-grained immune response in patients after treatment based on 36 clinical, tumor, and circulating molecular features of each patient collected prior to treatment. Our predicted immune response is then used to anticipate durable clinical benefit (DCB) of treatment. We analyze the bladder cancer patient data of Snyder et al. using the elastic net high-dimensional regression procedure and assess accuracy using leave-one-out cross-validation (LOOCV). Results: In held-out patients, the elastic net procedure explains 80% of the variance in immune response as measured by the log number of T cell clones in the tumor that expand in the blood post-therapy. Moreover, if patients are triaged according to held-out predicted expansion, only 34% of non-DCB patients need be treated to ensure that 100% of DCB patients are treated. In contrast, using PD-L1 staining or mutation load alone, one must treat at least 75% of non-DCB patients to ensure that all DCB patients receive treatment. The final elastic net model fit to the entire patient cohort retains 20 of the 36 input features, a mix of clinical, tumor, and circulating patient attributes. In addition, each class of features contributes significantly to the estimated accuracy of the procedure: when tumor features are excluded as inputs, the variance explained in held-out patients drops to 26%; when circulating features are excluded, the explained variance drops to 13%; and when clinical features are excluded, the explained variance drops to 0%. Conclusions: In a multifactorial model, tumor, circulating, and clinical features all contribute significantly to the accurate prediction of fine-grained immune response. Moreover, the accurate prediction of this immune response may improve our ability to anticipate clinical benefit.
ER -